If you are worried about economic inequality, you should be worried about regulation

July 21, 2020
By: Ethan Greist

This summer, Americans witnessed how mass dissatisfaction with the economic and personal costs of the coronavirus mingled with long-standing frustration regarding police misconduct to generate large-scale protests in the wake of the death of George Floyd in the custody of Minneapolis police.

While the proximate cause of these protests was undoubtedly a simmering outrage over the behavior of police, the issue of income inequality lurks in the background of the larger political discussion. Many of the blights of 2020 —police brutality, disease, unemployment, economic recession—do not affect people equally across all income levels, a phenomenon that economists (including some Mercatus scholars) and political scientists have been studying for decades.  Recently, these scholars have been devoting a greater attention to the understudied relationship between economic inequality and regulation. As a project that was intended to provide data for all manner of regulatory and policy research, QuantGov (and the data produced under its name) has featured prominently in a number of studies written on this topic. Today, I would like to take some time to chronicle this research.

On the grounds of economic theory, the impact of regulations on income inequality and social welfare are ambiguous. While well-designed regulations may mitigate negative externalities such as a pollution, as posited by the classic “public interest” theory of regulation, modern theories of regulation cast doubt on the efficacy of many regulations. For example, the Public Choice school of thought maintains that public interest groups can effectively lobby government policymakers to create laws and regulations which promote the interest of lobbyists at public expense. Moreover, the impact of regulation on the distribution of income hinges critically on the dispersed positive and negative impacts of the rules. In other words, all regulations carry costs and benefits that aren’t necessarily equally distributed between the rich and poor. This is true regardless of whether a given regulation is well-intended or part of a rent-seeking effort by lobbyists.

For example, a regulation might subsidize the price of a staple product of low-income households with the tax revenue from high-income earners, thus reducing inequality in consumption. Conversely, another regulation may drive-up firms’ compliance costs which are passed on to customers in the form of higher prices and employees in the form of lower wages. Regulation may also result in the erection of barriers to hiring employees, practicing an occupation, or starting a business in the first place. These latter forms of regulation tend to have a disproportionate impact on lower income individuals, and are thus contributors to economic inequality. Whether the effect of regulations is, on balance, regressive, progressive, or neutral is ultimately a question that must be answered by empirical research.

One potential answer to that question is suggested by a new paper titled “Regulations and Income Inequality,” by Dustin Chambers and Colin O’Reilly. Chambers and O’Reilly use the QuantGov FRASE index to measure how the incidence of federal regulation is associated with inequality across US states. They employ the Gini coefficient - an index which quantifies inequality by calculating the dispersion in incomes within given country, where a “0” represents “perfect equality” of income and the index approaches “1” as the discrepancy in income levels becomes larger - as their measure of inequality. The FRASE (Federal Regulation And State Enterprise) index measures how the burden of federal regulation differs across states by weighting the number of regulatory restrictions on each industry to the relative importance of those industries to a state’s economic composition. For example, federal regulations on agriculture will be more heavily weighted in states in which agriculture represents a larger share of the state economy. After controlling for the impact of other variables, Chambers and O’Reilly discovered that a 10% increase in the incidence of federal regulation is associated with a 0.5% increase in income inequality. In a similar study employing the FRASE index, Dustin Chambers, Patrick McLaughlin, and Laura Stanley investigated the relationship between federal regulation and poverty rates across states. They find that states with a higher incidence of federal regulation also tend to have a higher poverty rate. Specifically, a 10% increase in the federal regulatory burden on a state is associated with a 2.5% increase in the poverty rate. Taken together, these studies predict that a doubling of the effective federal regulatory burden on a state is associated with a 5% higher Gini coefficient and a 25% higher poverty rate.

In addition to these interesting results generated with the help of the FRASE dataset, scholars working in this literature have pulled data from other QuantGov projects to help study the relationship between regulation, wages, and prices. This research is important to the inequality literature because increased prices and decreased wages are both widely known to have a regressive effect. “How do federal regulations affect consumer prices?” by Dustin Chambers, Courtney Collins, and Alan Krause combined federal RegData with statistics on consumer spending and prices of consumer goods to assess the relationship between industry regulation and consumer prices. The authors discovered that 1) an increase in the number of regulatory restrictions levied on a given industry is associated with increased consumer prices for products in that industry and 2) low income households spend a disproportionate share of their income (compared to high income households) on goods that are more heavily regulated and thus, pricier. This means that the increase in prices associated with higher regulations are felt disproportionately by the poorest Americans. A parallel paper by James Bailey, Diana Thomas, and Joseph Anderson measures the effects of regulation on wages, and comes to the similar conclusion that regulations do in fact have a regressive effect on wages.

The final study that I will highlight within this growing body is a research is a paper titled “Regressive Effects of Regulation” by Diana Thomas. The paper focuses on the “public risk mitigation strategy” quality of regulation, by which quality and safety requirements are placed on the production (or features) of goods and services, with the goal of reducing the public’s risk of injury or death due to an unsafe product. The cost of this risk mitigation takes the form of both higher prices and lower wages that are passed down when producers are compelled to spend money to comply with the regulations. This alone contributes to inequality, as higher prices on consumer goods are generally regressive. However, Thomas also shows that the average annual cost per household for a mortality risk reduction of 1 in 10,000 was more than five times higher for regulation than it was for a plausible private risk mitigation strategy. In other words, for lower income households, regulation is five times more expensive per unit of risk reduction than is simply moving to a safer neighborhood. This is not true for wealthier households who are already situated in a safe neighborhood. For them, regulation is more cost effective than pursuing further private risk mitigation given that the cost of regulation is shared with lower income houses. The fact that lower income households are compelled to bear the costs of regulations that are a second-best option for them but are the better option for wealthy households that already live in relative safety shows that a significant share of regulation actually subsidizes the risk reduction preferences of higher income households with costs borne in large part by lower income households.

These examples showcase how the field of regulatory studies can provide insight into the very important and timely issue of a hidden cause of income inequality in the United States. They also demonstrate the utility of the datasets created by the QuantGov team for uncovering new insights about regulation. We encourage readers to take a look at these papers through the links embedded in the text above. More articles on this topic can also be found here, or browsed on our website page devoted to this topic.